Skip to main content

CoEvolution of Effective Observers and Observed Multi-agents System

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3630))

Abstract

This paper elaborates upon an idea and a development introduced and presented by Bersini in [1]. Roughly, by observing the search space of a combinatorial problem in a “clever” way, it can be drastically reduced. In order to discover this “clever way”, a second search process has to be engaged in the space of the observables. So two Genetic Algorithms (GAs) are intertwined to solve the whole problem: one in the original space and one in the space of observables of the original one. We are going to present and evaluate this idea on a Cellular Automata (CA) implementation of a binary numbers adder. The experiments show that the new algorithm, combining the two evolutionary searches, speeds up the research and/or increases the quality of the solutions in a significant way.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bersini, H.: Whatever emerges should be intrinsically useful. In: Artificial life 9,, pp. 226–231. The MIT Press, Cambridge (2004)

    Google Scholar 

  2. Crutchfield, J.: Is anything ever new? considering emergence. In: Cowan, G., Cowan, D.P.G., Melzner, D. (eds.) Integrative Themes. Volume XIX of Santa Fe Institute Studies in the Sciences of Complexity. Addison-Wesley Publishing Company, Reading (1994)

    Google Scholar 

  3. Packard, N.: Adaptation toward the edge of chaos. In: Dynamic Patterns in Complex Systems, pp. 293–301 (1988)

    Google Scholar 

  4. Andre, D., Bennett III, F.H., Koza, J.R.: Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, pp. 3–11. MIT Press, Cambridge (1996)

    Google Scholar 

  5. Crutchfield, J., Mitchell, M.: The evolution of emergent computation. Proceedings of the National Academy of Science 23, 103 (1995)

    Google Scholar 

  6. Cariani, P.: Emergence of new signal-primitives in neural networks. Intellectica 2, 95–143 (1997)

    Google Scholar 

  7. Baas, N.: Emergence, hierarchies, and hyperstructures. In: Langton, C. (ed.) Artificial life III. Santa Fe Institute Studies in the Sciences of Complexity, vol. XVII, pp. 515–537. Addison-Wesley Publishing Company, Reading (1994)

    Google Scholar 

  8. Bedeau, M.A.: Weak emergence. In: Tomberlin, J.E. (ed.) Philosophical Perspectives: Mind, Causation, and World, vol. 11, pp. 375–399. Blackwell Publishers, Oxford (1997)

    Google Scholar 

  9. Emmeche, C., Koppe, S., Stjernfelt, F.: Explaining emergence: Towards an ontology of levels. Journal for General Philosophy of Science 28, 83–119 (1997)

    Article  Google Scholar 

  10. Kubík, A.: Toward a formalization of emergence. Artificial Life 9, 41–65 (2003)

    Article  Google Scholar 

  11. Poundstone, W.: The Recursive Universe. Contemporary Books, Chicago (1985)

    Google Scholar 

  12. Rasmussen, S., Baas, N.A., Mayer, B., Nilsson, M., Olesen, M.W.: Ansatz for dynamical hierarchiesr. Artificial Life 7, 329–353 (2001)

    Article  Google Scholar 

  13. Wolfram, S.: Cellular Automata and Complexity. Addison-Wesley Publishing Company, Reading (1994)

    MATH  Google Scholar 

  14. Ronald, E.M.A., Sipper, M., Capcarrère, M.S.: Design, observation, surprise! a test of emergence. Artificial Life 5, 225–239 (1999)

    Article  Google Scholar 

  15. Sipper, M.: Computing with cellular automata: Three cases for nonuniformity. Physical Review E 57, 3589–3592 (1998)

    Article  Google Scholar 

  16. Bersini, H., Seront, G.: In search of a good evolution-optimization crossover. In: Männer, R., Manderick, B. (eds.) Second Conference Parallel Problem Solving from Nature, pp. 479–488. Elsevier Science Publishers, Amsterdam (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Philemotte, C., Bersini, H. (2005). CoEvolution of Effective Observers and Observed Multi-agents System. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_79

Download citation

  • DOI: https://doi.org/10.1007/11553090_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28848-0

  • Online ISBN: 978-3-540-31816-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics